ArticleSensors (Basel, Switzerland)2021
Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.BMC bioinformatics · 2024Article
- A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems.Sensors (Basel, Switzerland) · 2023Article
- Control and Optimisation of Power Grids Using Smart Meter Data: A Review.Sensors (Basel, Switzerland) · 2023Review
- Low-Power Transit Time-Based Gas Flow Sensor with Accuracy Optimization.Sensors (Basel, Switzerland) · 2022Article
- Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions.Sensors (Basel, Switzerland) · 2022Article
- Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development.Sensors (Basel, Switzerland) · 2022Review
- Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles.Sensors (Basel, Switzerland) · 2021Article
- Scientific Developments and New Technological Trajectories in Sensor Research.Sensors (Basel, Switzerland) · 2021Article
- Digital Twins of the Water Cooling System in a Power Plant Based on Fuzzy Logic.Sensors (Basel, Switzerland) · 2021Article
- An Ensembled Anomaly Detector for Wafer Fault Detection.Sensors (Basel, Switzerland) · 2021Article
- Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode.Sensors (Basel, Switzerland) · 2021Article
- Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System.Sensors (Basel, Switzerland) · 2021Article
- A Smart and Secure Logistics System Based on IoT and Cloud Technologies.Sensors (Basel, Switzerland) · 2021Article
- Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors.Sensors (Basel, Switzerland) · 2021Article
- Smart Node Networks Orchestration: A New E2E Approach for Analysis and Design for Agile 4.0 Implementation.Sensors (Basel, Switzerland) · 2021Article
- Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.